Characterising ocular injuries in competitive combat sports in Texas: a retrospective case–control study
Bibliographic record
Abstract
Objective This study aims to determine the incidence and impact of ocular injuries among the different combat sports disciplines of boxing, mixed martial arts (MMA), kickboxing and Muay Thai in Texas, USA. Design A case–control study was conducted to analyse retrospective postmatch physical reports from combat sports matches that took place in the state of Texas from January 2019 to January 2022. Ocular injuries and other match characteristics such as sport type and match outcome were identified by postmatch physical reports. Postmatch physical reports were collected from the Texas Department of Licensing and Regulation database. Statistical analysis was used to stratify injuries and compare the impact of injuries on match outcome. Setting Combat sports fighters in Texas, USA. Participants 3070 participants were included in the study. Participants were fighters who participated in combat sports matches in Texas, USA, between January 2019 and January 2022. Primary and secondary outcome measures The original plan was to measure the incidence of ocular injuries across different combat sports including boxing, MMA, kickboxing and Muay Thai. However, due to a limited sample size of kickboxing and Muay Thai matches, the ocular injury incidence was only measured for boxing and MMA. The association between ocular injury and match outcome was assessed using χ 2 statistical analysis. Results The respective incidence rates of ocular injuries in boxing and MMA were 9.7 and 12.2 injuries per 100 matches. The association between ocular injury and match outcome (win, lose or draw) was statistically significant in boxing but not statistically significant in MMA matches. Conclusions Our findings revealed that ocular injuries are significantly associated to losing a boxing match (p=0.011), but not associated to match outcome in MMA (p=0.232). Additionally, MMA matches report a larger variety of ocular injuries compared with boxing matches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".